Predators can strongly influence disease transmission and evolution, particularly when they prey selectively on infected hosts. Although selective predation has been observed in numerous systems, why predators select infected prey remains poorly understood. Here, we use a mathematical model of predator vision to test a longstanding hypothesis about the mechanistic basis of selective predation in a Daphnia-microparasite system, which serves as a model for the ecology and evolution of infectious diseases. Bluegill sunfish feed selectively on Daphnia infected by a variety of parasites, particularly in water uncolored by dissolved organic carbon. The leading hypothesis for selective predation in this system is that infection-induced changes in the transparency of Daphnia render them more visible to bluegill. Rigorously evaluating this hypothesis requires that we quantify the effect of infection on the visibility of prey from the predator's perspective, rather than our own. Using a model of the bluegill visual system, we show that three common parasites, Metschnikowia bicuspidata, Pasteuria ramosa and Spirobacillus cienkowskii, decrease the transparency of Daphnia, rendering infected Daphnia darker against a background of downwelling light. As a result of this increased brightness contrast, bluegill can see infected Daphnia at greater distances than uninfected Daphnia - between 19-33% further, depending on the parasite. Pasteuria and Spirobacillus also increase the chromatic contrast of Daphnia. These findings lend support to the hypothesis that selective predation by fish on infected Daphnia could result from the effects of infection on Daphnia's visibility. However, contrary to expectations, the visibility of Daphnia was not strongly impacted by water color in our model. Our work demonstrates that models of animal visual systems can be useful in understanding ecological interactions that impact disease transmission.

Data was collected as described in the paper. We discuss the data collection briefly below and note how the data were processed. 
Lake irradiance data - Data was collected in the upper part of the water column of two lakes, North Lake and Gosling Lake in Livingston County, Michigan. Measurements were taken at a depth of 50cm in the littoral zone and at 50cm & 150cm in the pelagic zone. A spectroradiometer (Ocean Optics S2000) connected to a patch cord (Ocean Optics QP400 -2 UV-VIS), which was in turn connected to a cosine corrector (Ocean Optics CC-3 DA). 

In the uploaded dataset, there is a column containing the raw data (raw_irradiance) and another with the smoothed data (smoothed_irradiance), which was used in the analysis. The raw data and smoothed data are plotted as transparent points and a smooth line in Fig. 2B of the paper, respectively. Raw data was smoothed using the loess() function in R, with a degree of 2 and span of 0.12. After smoothing, negative values were rounded to zero. We made two measurements of the downwelling irradiance in the littoral part of Gosling lake. For this location, therefore the two spectra were averaged prior to smoothing and the mean of the raw spectra is given as the raw value in "raw_irradiance column" and is plotted as the raw data points in Figure 2B. We give the two different spectra measured in the littoral part of Gosling lake in an additional sheet.

Daphnia data We measured light transmission through the thorax of Daphnia dentifera infected with three different parasites. Briefly, we connected a compound light microscope (Olympus BX53) to the aforementioned spectroradiometer via a patch cord and SMA connector, attached to the microscope's trinocular port. Daphnia were then illuminated from behind on the microscope and transmission of light measured. 

In the uploaded data set, there is a column containing the raw data (raw_transmission) and another with the smoothed data (smoothed_transmission), which was used in the analysis. The raw data and smoothed data are plotted as transparent points and a smooth line in Fig. 2C of the paper, respectively. Raw data was smoothed using the procspec() function of the pavo2 package in R, with a span of 0.12. Negative values were rounded to 0 using the argument fix.neg="zero".